Title

A robust inverse regression estimator

Authors

Authors

L. Q. Ni;R. D. Cook

Comments

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Abbreviated Journal Title

Stat. Probab. Lett.

Keywords

central subspace; inverse regression estimator; sufficient dimension; reduction; SUFFICIENT DIMENSION REDUCTION; Statistics & Probability

Abstract

A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410-428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. In this article, we propose a robust version of the IRE that only uses second moments of the predictor for estimation and inference, leading to better small sample results. (c) 2006 Elsevier B.V. All rights reserved.

Journal Title

Statistics & Probability Letters

Volume

77

Issue/Number

3

Publication Date

1-1-2007

Document Type

Article

Language

English

First Page

343

Last Page

349

WOS Identifier

WOS:000243660000015

ISSN

0167-7152

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